Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations57000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 MiB
Average record size in memory136.0 B

Variable types

Numeric10
Categorical7

Alerts

Aircraft Type is highly overall correlated with Destination and 3 other fieldsHigh correlation
Arrival Date & Time is highly overall correlated with Departure Date & Time and 1 other fieldsHigh correlation
Base Fare (BDT) is highly overall correlated with Duration (hrs) and 2 other fieldsHigh correlation
Departure Date & Time is highly overall correlated with Arrival Date & Time and 1 other fieldsHigh correlation
Destination is highly overall correlated with Aircraft Type and 1 other fieldsHigh correlation
Destination Name is highly overall correlated with Aircraft Type and 3 other fieldsHigh correlation
Duration (hrs) is highly overall correlated with Aircraft Type and 5 other fieldsHigh correlation
Seasonality is highly overall correlated with Arrival Date & Time and 1 other fieldsHigh correlation
Source is highly overall correlated with Source NameHigh correlation
Source Name is highly overall correlated with SourceHigh correlation
Stopovers is highly overall correlated with Aircraft Type and 2 other fieldsHigh correlation
Tax & Surcharge (BDT) is highly overall correlated with Base Fare (BDT) and 2 other fieldsHigh correlation
Total Fare (BDT) is highly overall correlated with Base Fare (BDT) and 2 other fieldsHigh correlation
Seasonality is highly imbalanced (54.9%) Imbalance
Departure Date & Time is uniformly distributed Uniform
Arrival Date & Time is uniformly distributed Uniform
Base Fare (BDT) has unique values Unique
Total Fare (BDT) has unique values Unique
Airline has 2217 (3.9%) zeros Zeros
Source has 7102 (12.5%) zeros Zeros
Destination has 3036 (5.3%) zeros Zeros

Reproduction

Analysis started2025-05-06 07:54:53.136588
Analysis finished2025-05-06 07:55:10.174723
Duration17.04 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Airline
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.914684
Minimum0
Maximum23
Zeros2217
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size445.4 KiB
2025-05-06T13:25:10.256879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.0839159
Coefficient of variation (CV)0.59455339
Kurtosis-1.247484
Mean11.914684
Median Absolute Deviation (MAD)6
Skewness-0.043663303
Sum679137
Variance50.181865
MonotonicityNot monotonic
2025-05-06T13:25:10.371694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
22 4496
 
7.9%
23 2368
 
4.2%
13 2368
 
4.2%
9 2346
 
4.1%
4 2344
 
4.1%
7 2327
 
4.1%
17 2321
 
4.1%
20 2316
 
4.1%
3 2312
 
4.1%
1 2304
 
4.0%
Other values (14) 31498
55.3%
ValueCountFrequency (%)
0 2217
3.9%
1 2304
4.0%
2 2280
4.0%
3 2312
4.1%
4 2344
4.1%
5 2209
3.9%
6 2282
4.0%
7 2327
4.1%
8 2267
4.0%
9 2346
4.1%
ValueCountFrequency (%)
23 2368
4.2%
22 4496
7.9%
21 2220
3.9%
20 2316
4.1%
19 2201
3.9%
18 2279
4.0%
17 2321
4.1%
16 2267
4.0%
15 2268
4.0%
14 2292
4.0%

Source
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4971754
Minimum0
Maximum7
Zeros7102
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size445.4 KiB
2025-05-06T13:25:10.452205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2926687
Coefficient of variation (CV)0.65557727
Kurtosis-1.2388208
Mean3.4971754
Median Absolute Deviation (MAD)2
Skewness0.0042053154
Sum199339
Variance5.2563299
MonotonicityNot monotonic
2025-05-06T13:25:10.544063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 7241
12.7%
7 7179
12.6%
4 7149
12.5%
3 7146
12.5%
0 7102
12.5%
5 7091
12.4%
2 7049
12.4%
6 7043
12.4%
ValueCountFrequency (%)
0 7102
12.5%
1 7241
12.7%
2 7049
12.4%
3 7146
12.5%
4 7149
12.5%
5 7091
12.4%
6 7043
12.4%
7 7179
12.6%
ValueCountFrequency (%)
7 7179
12.6%
6 7043
12.4%
5 7091
12.4%
4 7149
12.5%
3 7146
12.5%
2 7049
12.4%
1 7241
12.7%
0 7102
12.5%

Source Name
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size445.4 KiB
Shah Amanat International Airport, Chittagong
7241 
Osmani International Airport, Sylhet
7179 
Jessore Airport
7149 
Hazrat Shahjalal International Airport, Dhaka
7146 
Barisal Airport
7102 
Other values (3)
21183 

Length

Max length45
Median length36
Mean length27.577719
Min length15

Characters and Unicode

Total characters1571930
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCox's Bazar Airport
2nd rowBarisal Airport
3rd rowOsmani International Airport, Sylhet
4th rowShah Makhdum Airport, Rajshahi
5th rowSaidpur Airport

Common Values

ValueCountFrequency (%)
Shah Amanat International Airport, Chittagong 7241
12.7%
Osmani International Airport, Sylhet 7179
12.6%
Jessore Airport 7149
12.5%
Hazrat Shahjalal International Airport, Dhaka 7146
12.5%
Barisal Airport 7102
12.5%
Shah Makhdum Airport, Rajshahi 7091
12.4%
Cox's Bazar Airport 7049
12.4%
Saidpur Airport 7043
12.4%

Length

2025-05-06T13:25:10.672381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T13:25:10.811801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
airport 57000
29.6%
international 21566
 
11.2%
shah 14332
 
7.4%
amanat 7241
 
3.8%
chittagong 7241
 
3.8%
osmani 7179
 
3.7%
sylhet 7179
 
3.7%
jessore 7149
 
3.7%
hazrat 7146
 
3.7%
shahjalal 7146
 
3.7%
Other values (7) 49571
25.7%

Most occurring characters

ValueCountFrequency (%)
a 193006
12.3%
r 171055
10.9%
t 136180
 
8.7%
135750
 
8.6%
i 114222
 
7.3%
o 100005
 
6.4%
n 86359
 
5.5%
h 85795
 
5.5%
A 64241
 
4.1%
p 64043
 
4.1%
Other values (24) 421274
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1571930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 193006
12.3%
r 171055
10.9%
t 136180
 
8.7%
135750
 
8.6%
i 114222
 
7.3%
o 100005
 
6.4%
n 86359
 
5.5%
h 85795
 
5.5%
A 64241
 
4.1%
p 64043
 
4.1%
Other values (24) 421274
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1571930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 193006
12.3%
r 171055
10.9%
t 136180
 
8.7%
135750
 
8.6%
i 114222
 
7.3%
o 100005
 
6.4%
n 86359
 
5.5%
h 85795
 
5.5%
A 64241
 
4.1%
p 64043
 
4.1%
Other values (24) 421274
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1571930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 193006
12.3%
r 171055
10.9%
t 136180
 
8.7%
135750
 
8.6%
i 114222
 
7.3%
o 100005
 
6.4%
n 86359
 
5.5%
h 85795
 
5.5%
A 64241
 
4.1%
p 64043
 
4.1%
Other values (24) 421274
26.8%

Destination
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5062456
Minimum0
Maximum19
Zeros3036
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size445.4 KiB
2025-05-06T13:25:10.951729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q314
95-th percentile18
Maximum19
Range19
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.7256985
Coefficient of variation (CV)0.60230913
Kurtosis-1.1756461
Mean9.5062456
Median Absolute Deviation (MAD)5
Skewness-0.012700636
Sum541856
Variance32.783624
MonotonicityNot monotonic
2025-05-06T13:25:11.068344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
10 3071
 
5.4%
6 3069
 
5.4%
11 3066
 
5.4%
16 3063
 
5.4%
0 3036
 
5.3%
8 3005
 
5.3%
14 2968
 
5.2%
13 2968
 
5.2%
9 2940
 
5.2%
2 2938
 
5.2%
Other values (10) 26876
47.2%
ValueCountFrequency (%)
0 3036
5.3%
1 2641
4.6%
2 2938
5.2%
3 2613
4.6%
4 2652
4.7%
5 2591
4.5%
6 3069
5.4%
7 2923
5.1%
8 3005
5.3%
9 2940
5.2%
ValueCountFrequency (%)
19 2692
4.7%
18 2921
5.1%
17 2644
4.6%
16 3063
5.4%
15 2640
4.6%
14 2968
5.2%
13 2968
5.2%
12 2559
4.5%
11 3066
5.4%
10 3071
5.4%

Destination Name
Categorical

High correlation 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size445.4 KiB
King Abdulaziz International Airport, Jeddah
 
3071
Indira Gandhi International Airport, Delhi
 
3069
John F. Kennedy International Airport, New York
 
3066
Singapore Changi Airport
 
3063
Suvarnabhumi Airport, Bangkok
 
3036
Other values (15)
41695 

Length

Max length57
Median length34
Mean length31.883737
Min length15

Characters and Unicode

Total characters1817373
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNetaji Subhas Chandra Bose International Airport, Kolkata
2nd rowShah Amanat International Airport, Chittagong
3rd rowKuala Lumpur International Airport
4th rowHazrat Shahjalal International Airport, Dhaka
5th rowToronto Pearson International Airport

Common Values

ValueCountFrequency (%)
King Abdulaziz International Airport, Jeddah 3071
 
5.4%
Indira Gandhi International Airport, Delhi 3069
 
5.4%
John F. Kennedy International Airport, New York 3066
 
5.4%
Singapore Changi Airport 3063
 
5.4%
Suvarnabhumi Airport, Bangkok 3036
 
5.3%
Dubai International Airport 3005
 
5.3%
London Heathrow Airport 2968
 
5.2%
Kuala Lumpur International Airport 2968
 
5.2%
Istanbul Airport 2940
 
5.2%
Netaji Subhas Chandra Bose International Airport, Kolkata 2938
 
5.2%
Other values (10) 26876
47.2%

Length

2025-05-06T13:25:11.200147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
airport 57000
25.8%
international 31857
 
14.4%
shah 5253
 
2.4%
abdulaziz 3071
 
1.4%
jeddah 3071
 
1.4%
king 3071
 
1.4%
indira 3069
 
1.4%
delhi 3069
 
1.4%
gandhi 3069
 
1.4%
f 3066
 
1.4%
Other values (37) 105468
47.7%

Most occurring characters

ValueCountFrequency (%)
a 190466
 
10.5%
r 185894
 
10.2%
164064
 
9.0%
n 151750
 
8.3%
t 148541
 
8.2%
o 138299
 
7.6%
i 132541
 
7.3%
e 69833
 
3.8%
p 65675
 
3.6%
A 62684
 
3.4%
Other values (36) 507626
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1817373
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 190466
 
10.5%
r 185894
 
10.2%
164064
 
9.0%
n 151750
 
8.3%
t 148541
 
8.2%
o 138299
 
7.6%
i 132541
 
7.3%
e 69833
 
3.8%
p 65675
 
3.6%
A 62684
 
3.4%
Other values (36) 507626
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1817373
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 190466
 
10.5%
r 185894
 
10.2%
164064
 
9.0%
n 151750
 
8.3%
t 148541
 
8.2%
o 138299
 
7.6%
i 132541
 
7.3%
e 69833
 
3.8%
p 65675
 
3.6%
A 62684
 
3.4%
Other values (36) 507626
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1817373
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 190466
 
10.5%
r 185894
 
10.2%
164064
 
9.0%
n 151750
 
8.3%
t 148541
 
8.2%
o 138299
 
7.6%
i 132541
 
7.3%
e 69833
 
3.8%
p 65675
 
3.6%
A 62684
 
3.4%
Other values (36) 507626
27.9%

Departure Date & Time
Real number (ℝ)

High correlation  Uniform 

Distinct54126
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27042.819
Minimum0
Maximum54125
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size445.4 KiB
2025-05-06T13:25:11.343824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2794.95
Q113583.75
median27033.5
Q340465.25
95-th percentile51341.05
Maximum54125
Range54125
Interquartile range (IQR)26881.5

Descriptive statistics

Standard deviation15553.65
Coefficient of variation (CV)0.57514897
Kurtosis-1.1944516
Mean27042.819
Median Absolute Deviation (MAD)13440.5
Skewness0.0022702668
Sum1.5414407 × 109
Variance2.4191601 × 108
MonotonicityNot monotonic
2025-05-06T13:25:11.531514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8752 4
 
< 0.1%
28039 4
 
< 0.1%
40563 4
 
< 0.1%
29799 3
 
< 0.1%
27083 3
 
< 0.1%
25833 3
 
< 0.1%
28447 3
 
< 0.1%
9933 3
 
< 0.1%
25162 3
 
< 0.1%
12553 3
 
< 0.1%
Other values (54116) 56967
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
54125 1
< 0.1%
54124 1
< 0.1%
54123 1
< 0.1%
54122 1
< 0.1%
54121 1
< 0.1%
54120 1
< 0.1%
54119 1
< 0.1%
54118 1
< 0.1%
54117 1
< 0.1%
54116 1
< 0.1%

Arrival Date & Time
Real number (ℝ)

High correlation  Uniform 

Distinct56944
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28472.917
Minimum0
Maximum56943
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size445.4 KiB
2025-05-06T13:25:11.944418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2847.95
Q114238.75
median28476.5
Q342707.25
95-th percentile54094.05
Maximum56943
Range56943
Interquartile range (IQR)28468.5

Descriptive statistics

Standard deviation16437.519
Coefficient of variation (CV)0.57730366
Kurtosis-1.199979
Mean28472.917
Median Absolute Deviation (MAD)14234.5
Skewness-0.00020648293
Sum1.6229563 × 109
Variance2.7019204 × 108
MonotonicityNot monotonic
2025-05-06T13:25:12.099188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7225 2
 
< 0.1%
41026 2
 
< 0.1%
2891 2
 
< 0.1%
21436 2
 
< 0.1%
22621 2
 
< 0.1%
17489 2
 
< 0.1%
51218 2
 
< 0.1%
16404 2
 
< 0.1%
53865 2
 
< 0.1%
2786 2
 
< 0.1%
Other values (56934) 56980
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
56943 1
< 0.1%
56942 1
< 0.1%
56941 1
< 0.1%
56940 1
< 0.1%
56939 1
< 0.1%
56938 1
< 0.1%
56937 1
< 0.1%
56936 1
< 0.1%
56935 1
< 0.1%
56934 1
< 0.1%

Duration (hrs)
Real number (ℝ)

High correlation 

Distinct53135
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.994955
Minimum0.5
Maximum15.831719
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size445.4 KiB
2025-05-06T13:25:12.258415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q11.0037448
median2.6446559
Q35.4901038
95-th percentile14.036677
Maximum15.831719
Range15.331719
Interquartile range (IQR)4.4863591

Descriptive statistics

Standard deviation4.0940425
Coefficient of variation (CV)1.0248032
Kurtosis1.1328279
Mean3.994955
Median Absolute Deviation (MAD)1.7282812
Skewness1.4733512
Sum227712.44
Variance16.761184
MonotonicityNot monotonic
2025-05-06T13:25:12.444619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 3866
 
6.8%
1.241106695 1
 
< 0.1%
13.92327945 1
 
< 0.1%
15.0919049 1
 
< 0.1%
1.684250408 1
 
< 0.1%
0.7830074766 1
 
< 0.1%
0.8544008393 1
 
< 0.1%
1.039055123 1
 
< 0.1%
0.7094425783 1
 
< 0.1%
0.6487548189 1
 
< 0.1%
Other values (53125) 53125
93.2%
ValueCountFrequency (%)
0.5 3866
6.8%
0.500009115 1
 
< 0.1%
0.5000414676 1
 
< 0.1%
0.5000737912 1
 
< 0.1%
0.5000888133 1
 
< 0.1%
0.5001213664 1
 
< 0.1%
0.500211606 1
 
< 0.1%
0.5002133275 1
 
< 0.1%
0.5002147752 1
 
< 0.1%
0.5002597916 1
 
< 0.1%
ValueCountFrequency (%)
15.83171913 1
< 0.1%
15.82217788 1
< 0.1%
15.8190965 1
< 0.1%
15.80857125 1
< 0.1%
15.80770546 1
< 0.1%
15.80734819 1
< 0.1%
15.79673801 1
< 0.1%
15.79234489 1
< 0.1%
15.79163524 1
< 0.1%
15.77406545 1
< 0.1%

Stopovers
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size445.4 KiB
2.0
36642 
0.0
17400 
1.0
 
2958

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters171000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row0.0
4th row2.0
5th row0.0

Common Values

ValueCountFrequency (%)
2.0 36642
64.3%
0.0 17400
30.5%
1.0 2958
 
5.2%

Length

2025-05-06T13:25:12.595147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T13:25:12.700983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 36642
64.3%
0.0 17400
30.5%
1.0 2958
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 74400
43.5%
. 57000
33.3%
2 36642
21.4%
1 2958
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 74400
43.5%
. 57000
33.3%
2 36642
21.4%
1 2958
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 74400
43.5%
. 57000
33.3%
2 36642
21.4%
1 2958
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 74400
43.5%
. 57000
33.3%
2 36642
21.4%
1 2958
 
1.7%

Aircraft Type
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size445.4 KiB
0.0
23970 
4.0
9092 
2.0
8972 
1.0
7572 
3.0
7394 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters171000
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row4.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 23970
42.1%
4.0 9092
 
16.0%
2.0 8972
 
15.7%
1.0 7572
 
13.3%
3.0 7394
 
13.0%

Length

2025-05-06T13:25:12.841302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T13:25:12.987986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23970
42.1%
4.0 9092
 
16.0%
2.0 8972
 
15.7%
1.0 7572
 
13.3%
3.0 7394
 
13.0%

Most occurring characters

ValueCountFrequency (%)
0 80970
47.4%
. 57000
33.3%
4 9092
 
5.3%
2 8972
 
5.2%
1 7572
 
4.4%
3 7394
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 80970
47.4%
. 57000
33.3%
4 9092
 
5.3%
2 8972
 
5.2%
1 7572
 
4.4%
3 7394
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 80970
47.4%
. 57000
33.3%
4 9092
 
5.3%
2 8972
 
5.2%
1 7572
 
4.4%
3 7394
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 80970
47.4%
. 57000
33.3%
4 9092
 
5.3%
2 8972
 
5.2%
1 7572
 
4.4%
3 7394
 
4.3%

Class
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size445.4 KiB
1.0
19112 
2.0
18979 
0.0
18909 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters171000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 19112
33.5%
2.0 18979
33.3%
0.0 18909
33.2%

Length

2025-05-06T13:25:13.098226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T13:25:13.177185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 19112
33.5%
2.0 18979
33.3%
0.0 18909
33.2%

Most occurring characters

ValueCountFrequency (%)
0 75909
44.4%
. 57000
33.3%
1 19112
 
11.2%
2 18979
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 75909
44.4%
. 57000
33.3%
1 19112
 
11.2%
2 18979
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 75909
44.4%
. 57000
33.3%
1 19112
 
11.2%
2 18979
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 75909
44.4%
. 57000
33.3%
1 19112
 
11.2%
2 18979
 
11.1%

Booking Source
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size445.4 KiB
0.0
19111 
1.0
18966 
2.0
18923 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters171000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 19111
33.5%
1.0 18966
33.3%
2.0 18923
33.2%

Length

2025-05-06T13:25:13.276567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T13:25:13.356772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 19111
33.5%
1.0 18966
33.3%
2.0 18923
33.2%

Most occurring characters

ValueCountFrequency (%)
0 76111
44.5%
. 57000
33.3%
1 18966
 
11.1%
2 18923
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 76111
44.5%
. 57000
33.3%
1 18966
 
11.1%
2 18923
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 76111
44.5%
. 57000
33.3%
1 18966
 
11.1%
2 18923
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 76111
44.5%
. 57000
33.3%
1 18966
 
11.1%
2 18923
 
11.1%

Base Fare (BDT)
Real number (ℝ)

High correlation  Unique 

Distinct57000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58899.557
Minimum1600.9757
Maximum449222.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size445.4 KiB
2025-05-06T13:25:13.519654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1600.9757
5-th percentile2957.245
Q18856.317
median31615.997
Q385722.93
95-th percentile219595.4
Maximum449222.93
Range447621.96
Interquartile range (IQR)76866.613

Descriptive statistics

Standard deviation68840.614
Coefficient of variation (CV)1.1687798
Kurtosis2.1219469
Mean58899.557
Median Absolute Deviation (MAD)26014.887
Skewness1.6145098
Sum3.3572747 × 109
Variance4.7390302 × 109
MonotonicityNot monotonic
2025-05-06T13:25:13.652980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5831.070839 1
 
< 0.1%
21131.22502 1
 
< 0.1%
11605.39547 1
 
< 0.1%
39882.49935 1
 
< 0.1%
4435.60734 1
 
< 0.1%
59243.80615 1
 
< 0.1%
5036.39416 1
 
< 0.1%
7397.669874 1
 
< 0.1%
9988.870484 1
 
< 0.1%
5791.095528 1
 
< 0.1%
Other values (56990) 56990
> 99.9%
ValueCountFrequency (%)
1600.975688 1
< 0.1%
1601.196913 1
< 0.1%
1604.132096 1
< 0.1%
1605.890639 1
< 0.1%
1605.984292 1
< 0.1%
1606.465648 1
< 0.1%
1606.923044 1
< 0.1%
1608.644603 1
< 0.1%
1609.274256 1
< 0.1%
1610.812561 1
< 0.1%
ValueCountFrequency (%)
449222.9338 1
< 0.1%
414657.0418 1
< 0.1%
411490.1019 1
< 0.1%
404095.4385 1
< 0.1%
401585.0235 1
< 0.1%
397214.7373 1
< 0.1%
395859.1689 1
< 0.1%
395147.8565 1
< 0.1%
395129.7561 1
< 0.1%
392465.5757 1
< 0.1%

Tax & Surcharge (BDT)
Real number (ℝ)

High correlation 

Distinct35969
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11448.238
Minimum200
Maximum73383.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size445.4 KiB
2025-05-06T13:25:13.774641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile200
Q1200
median9450.9405
Q317513.046
95-th percentile37626.637
Maximum73383.44
Range73183.44
Interquartile range (IQR)17313.046

Descriptive statistics

Standard deviation12124.344
Coefficient of variation (CV)1.0590576
Kurtosis0.823038
Mean11448.238
Median Absolute Deviation (MAD)9250.9405
Skewness1.1331808
Sum6.5254959 × 108
Variance1.4699973 × 108
MonotonicityNot monotonic
2025-05-06T13:25:13.907165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 21032
36.9%
17460.89202 1
 
< 0.1%
11982.3749 1
 
< 0.1%
14886.57092 1
 
< 0.1%
7792.022037 1
 
< 0.1%
17246.04328 1
 
< 0.1%
19542.74682 1
 
< 0.1%
8362.415852 1
 
< 0.1%
10542.2712 1
 
< 0.1%
12351.40921 1
 
< 0.1%
Other values (35959) 35959
63.1%
ValueCountFrequency (%)
200 21032
36.9%
2601.672051 1
 
< 0.1%
2614.723466 1
 
< 0.1%
2620.41001 1
 
< 0.1%
2631.617098 1
 
< 0.1%
2648.996822 1
 
< 0.1%
2650.737015 1
 
< 0.1%
2685.462829 1
 
< 0.1%
2688.35607 1
 
< 0.1%
2723.854876 1
 
< 0.1%
ValueCountFrequency (%)
73383.44007 1
< 0.1%
68198.55626 1
< 0.1%
67723.51528 1
< 0.1%
65582.21059 1
< 0.1%
65269.46342 1
< 0.1%
64869.83636 1
< 0.1%
64670.75249 1
< 0.1%
64361.01576 1
< 0.1%
64237.75353 1
< 0.1%
63449.05639 1
< 0.1%

Total Fare (BDT)
Real number (ℝ)

High correlation  Unique 

Distinct57000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71030.316
Minimum1800.9757
Maximum558987.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size445.4 KiB
2025-05-06T13:25:14.052075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1800.9757
5-th percentile3176.3919
Q19602.6998
median41307.545
Q3103800.91
95-th percentile258788.93
Maximum558987.33
Range557186.36
Interquartile range (IQR)94198.207

Descriptive statistics

Standard deviation81769.2
Coefficient of variation (CV)1.1511873
Kurtosis2.0868178
Mean71030.316
Median Absolute Deviation (MAD)34632.691
Skewness1.5774652
Sum4.048728 × 109
Variance6.686202 × 109
MonotonicityNot monotonic
2025-05-06T13:25:14.218855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6031.070839 1
 
< 0.1%
26300.90877 1
 
< 0.1%
11805.39547 1
 
< 0.1%
51864.87425 1
 
< 0.1%
4635.60734 1
 
< 0.1%
74130.37707 1
 
< 0.1%
5236.39416 1
 
< 0.1%
7597.669874 1
 
< 0.1%
10188.87048 1
 
< 0.1%
5991.095528 1
 
< 0.1%
Other values (56990) 56990
> 99.9%
ValueCountFrequency (%)
1800.975688 1
< 0.1%
1801.196913 1
< 0.1%
1804.132096 1
< 0.1%
1805.890639 1
< 0.1%
1805.984292 1
< 0.1%
1806.465648 1
< 0.1%
1806.923044 1
< 0.1%
1808.644603 1
< 0.1%
1809.274256 1
< 0.1%
1810.812561 1
< 0.1%
ValueCountFrequency (%)
558987.3324 1
< 0.1%
546970.9229 1
< 0.1%
524278.7604 1
< 0.1%
522606.3738 1
< 0.1%
493383.9673 1
< 0.1%
489089.9208 1
< 0.1%
487614.4264 1
< 0.1%
487399.9001 1
< 0.1%
482855.598 1
< 0.1%
479213.6172 1
< 0.1%

Seasonality
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size445.4 KiB
2.0
44525 
3.0
10930 
1.0
 
942
0.0
 
603

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters171000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row3.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 44525
78.1%
3.0 10930
 
19.2%
1.0 942
 
1.7%
0.0 603
 
1.1%

Length

2025-05-06T13:25:14.343415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-06T13:25:14.411790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 44525
78.1%
3.0 10930
 
19.2%
1.0 942
 
1.7%
0.0 603
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 57603
33.7%
. 57000
33.3%
2 44525
26.0%
3 10930
 
6.4%
1 942
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 57603
33.7%
. 57000
33.3%
2 44525
26.0%
3 10930
 
6.4%
1 942
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 57603
33.7%
. 57000
33.3%
2 44525
26.0%
3 10930
 
6.4%
1 942
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 57603
33.7%
. 57000
33.3%
2 44525
26.0%
3 10930
 
6.4%
1 942
 
0.6%

Days Before Departure
Real number (ℝ)

Distinct90
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.460579
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size445.4 KiB
2025-05-06T13:25:14.516232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q123
median45
Q368
95-th percentile86
Maximum90
Range89
Interquartile range (IQR)45

Descriptive statistics

Standard deviation26.015657
Coefficient of variation (CV)0.57226848
Kurtosis-1.2079452
Mean45.460579
Median Absolute Deviation (MAD)23
Skewness0.0038742714
Sum2591253
Variance676.81439
MonotonicityNot monotonic
2025-05-06T13:25:14.651283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 684
 
1.2%
39 683
 
1.2%
23 679
 
1.2%
69 679
 
1.2%
66 674
 
1.2%
79 667
 
1.2%
28 667
 
1.2%
74 666
 
1.2%
45 666
 
1.2%
9 664
 
1.2%
Other values (80) 50271
88.2%
ValueCountFrequency (%)
1 639
1.1%
2 609
1.1%
3 618
1.1%
4 656
1.2%
5 619
1.1%
6 604
1.1%
7 647
1.1%
8 643
1.1%
9 664
1.2%
10 648
1.1%
ValueCountFrequency (%)
90 631
1.1%
89 641
1.1%
88 615
1.1%
87 633
1.1%
86 643
1.1%
85 600
1.1%
84 638
1.1%
83 647
1.1%
82 649
1.1%
81 612
1.1%

Interactions

2025-05-06T13:25:08.532473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:24:56.700806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:24:58.093489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:24:59.462852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:25:00.747596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:25:02.136816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:25:03.457969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:25:04.700045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:25:05.843189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:25:07.273484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:25:08.634321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:24:56.845863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:24:58.382601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:24:59.582502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:25:00.871788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:25:02.244393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:25:03.584141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:25:04.802610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:25:05.962193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:25:07.390422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:25:08.743670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:24:56.980872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:24:58.513422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:24:59.708620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T13:25:00.986716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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Correlations

2025-05-06T13:25:14.770497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Aircraft TypeAirlineArrival Date & TimeBase Fare (BDT)Booking SourceClassDays Before DepartureDeparture Date & TimeDestinationDestination NameDuration (hrs)SeasonalitySourceSource NameStopoversTax & Surcharge (BDT)Total Fare (BDT)
Aircraft Type1.0000.0000.0000.2900.0090.0050.0000.0000.5260.7070.6970.0050.0060.0060.6090.4100.295
Airline0.0001.000-0.002-0.0020.0120.0000.000-0.002-0.0000.0000.0010.000-0.0020.0020.004-0.001-0.001
Arrival Date & Time0.000-0.0021.0000.0200.0070.0050.2301.0000.0040.0020.0040.5890.0040.0000.0110.0140.017
Base Fare (BDT)0.290-0.0020.0201.0000.0000.405-0.0590.019-0.0020.2140.6260.110-0.0010.0000.2560.9570.997
Booking Source0.0090.0120.0070.0001.0000.0000.0000.0080.0030.0130.0130.0000.0040.0040.0050.0000.000
Class0.0050.0000.0050.4050.0001.0000.0050.0040.0000.0000.0000.0060.0070.0070.0010.4230.400
Days Before Departure0.0000.0000.230-0.0590.0000.0051.0000.2300.0030.0000.0010.0800.0040.0000.000-0.037-0.066
Departure Date & Time0.000-0.0021.0000.0190.0080.0040.2301.0000.0040.0000.0030.5870.0040.0000.0100.0130.016
Destination0.526-0.0000.004-0.0020.0030.0000.0030.0041.0001.0000.2920.000-0.0640.0600.4650.0370.004
Destination Name0.7070.0000.0020.2140.0130.0000.0000.0001.0001.0000.7450.0000.0870.0870.6080.2960.216
Duration (hrs)0.6970.0010.0040.6260.0130.0000.0010.0030.2920.7451.0000.0050.0290.0410.6060.6950.649
Seasonality0.0050.0000.5890.1100.0000.0060.0800.5870.0000.0000.0051.0000.0080.0080.0000.1060.086
Source0.006-0.0020.004-0.0010.0040.0070.0040.004-0.0640.0870.0290.0081.0001.0000.001-0.002-0.002
Source Name0.0060.0020.0000.0000.0040.0070.0000.0000.0600.0870.0410.0081.0001.0000.0010.0000.000
Stopovers0.6090.0040.0110.2560.0050.0010.0000.0100.4650.6080.6060.0000.0010.0011.0000.3750.263
Tax & Surcharge (BDT)0.410-0.0010.0140.9570.0000.423-0.0370.0130.0370.2960.6950.106-0.0020.0000.3751.0000.967
Total Fare (BDT)0.295-0.0010.0170.9970.0000.400-0.0660.0160.0040.2160.6490.086-0.0020.0000.2630.9671.000

Missing values

2025-05-06T13:25:09.702154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-06T13:25:09.930445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AirlineSourceSource NameDestinationDestination NameDeparture Date & TimeArrival Date & TimeDuration (hrs)StopoversAircraft TypeClassBooking SourceBase Fare (BDT)Tax & Surcharge (BDT)Total Fare (BDT)SeasonalityDays Before Departure
014.02.0Cox's Bazar Airport2.0Netaji Subhas Chandra Bose International Airport, Kolkata40813.043051.01.2195262.00.01.01.021131.2250215169.68375326300.9087752.010
16.00.0Barisal Airport3.0Shah Amanat International Airport, Chittagong4535.04626.00.6086382.00.02.02.011605.395471200.00000011805.3954712.014
25.07.0Osmani International Airport, Sylhet13.0Kuala Lumpur International Airport44667.047101.02.6896510.04.01.02.039882.49934911982.37490251864.8742513.083
318.05.0Shah Makhdum Airport, Rajshahi5.0Hazrat Shahjalal International Airport, Dhaka15397.016154.00.6860542.00.01.00.04435.607340200.0000004635.6073402.056
45.06.0Saidpur Airport18.0Toronto Pearson International Airport10340.010863.014.0556090.01.00.00.059243.80614614886.57092274130.3770682.090
511.07.0Osmani International Airport, Sylhet17.0Saidpur Airport18311.019235.01.0850662.00.00.02.05036.394160200.0000005236.3941602.070
622.06.0Saidpur Airport1.0Barisal Airport37384.039437.01.0781552.00.00.00.07397.669874200.0000007597.6698742.028
73.04.0Jessore Airport14.0London Heathrow Airport46786.049372.08.8803760.01.01.01.011946.8135837792.02203719738.8356203.012
88.06.0Saidpur Airport13.0Kuala Lumpur International Airport20988.022066.03.2962822.04.00.02.074973.62187117246.04328192219.6651522.023
922.03.0Hazrat Shahjalal International Airport, Dhaka6.0Indira Gandhi International Airport, Delhi8770.09127.02.5956202.04.02.00.0116951.64544519542.746817136494.3922612.041
AirlineSourceSource NameDestinationDestination NameDeparture Date & TimeArrival Date & TimeDuration (hrs)StopoversAircraft TypeClassBooking SourceBase Fare (BDT)Tax & Surcharge (BDT)Total Fare (BDT)SeasonalityDays Before Departure
569904.03.0Hazrat Shahjalal International Airport, Dhaka7.0Hamad International Airport, Doha4409.04515.03.9725072.04.02.00.038531.5456959779.73185448311.2775492.042
569915.05.0Shah Makhdum Airport, Rajshahi18.0Toronto Pearson International Airport3017.03112.012.8938480.01.01.02.027622.21739410143.33260937765.5500032.039
5699215.01.0Shah Amanat International Airport, Chittagong11.0John F. Kennedy International Airport, New York33120.034991.014.2531560.01.01.00.034824.60957311223.69143646048.3010092.032
5699318.07.0Osmani International Airport, Sylhet8.0Dubai International Airport37445.039518.04.2216162.02.02.02.0261460.04112443219.006169304679.0472922.041
569945.06.0Saidpur Airport11.0John F. Kennedy International Airport, New York38998.041224.013.6544210.01.01.00.024943.1469849741.47204834684.6190312.06
5699512.04.0Jessore Airport2.0Netaji Subhas Chandra Bose International Airport, Kolkata26082.027439.00.5000002.00.00.01.079974.47174813996.17076293970.6425112.051
5699612.01.0Shah Amanat International Airport, Chittagong2.0Netaji Subhas Chandra Bose International Airport, Kolkata32052.033763.01.2751452.00.02.01.0193471.36427731020.704642224492.0689182.031
569974.02.0Cox's Bazar Airport12.0Jessore Airport39437.041592.01.2165832.00.01.00.04375.365554200.0000004575.3655542.022
569985.06.0Saidpur Airport18.0Toronto Pearson International Airport41958.044332.013.9605020.01.01.00.040903.60268812135.54040353039.1430912.020
569992.03.0Hazrat Shahjalal International Airport, Dhaka15.0Shah Makhdum Airport, Rajshahi20750.021786.00.6487552.00.00.00.05831.070839200.0000006031.0708392.06